Operations Research

The Operations Research area has two themes of primary focus: computational geometry,
and stochastic optimization. The computational geometry group consists of Esther Arkin
and Joe Mitchell along with adjunct faculty Michael Bender, Jie Gao, and Steve Skiena
in Computer Science. The stochastic optimization group consists of Eugene Feinberg
and Jiaqiao Hu, along with adjunct professor Hussein Badr in Computer Science.

Computational GeometryEsther Arkin's primary research area is the design and analysis of algorithms that arise in network
optimization, computational geometry, graph theory, scheduling, robotics, geographic
information systems, computer graphics, manufacturing, and computer vision. Arkin
is interested in theoretical analysis of worst-case complexity of problems, especially
those that require optimization. She collaborates extensively with Joe Mitchell. For
more information, see
Arkin webpage.

Joe Mitchell's primary research area is computational geometry, applied to pratical problems in computer
graphics, visualization, robotics, manufacturing, geographic information systems,
and computer vision. Mitchell is one of the country's leaders in computational geometry,
which studies the design, analysis, and implementation of efficient algorithms to
solve geometric problems; in the 1990's, he chaired the National Science Foundation
advisory committee in computational geometry. A major current application is helping
air traffic controllers route airplanes around storm systems as they approach an airport.
For more information, see
Mitchell webpage.

Stochastic OptimizationEugene Feinberg works in stochastic methods of operations research and their industrial applications.
He is one of the leaders in Markov decision processes and its application to telecommunication,
manufacturing, transportation, service and to other man-made systems. He is also one
of the country's experts on optimizing electric energy transmission and forecasting
energy demand. For more information, see
Feinberg webpage.

Jiaqiao Hu's research is focused on designing and analyzing randomized algorithms for solving
Markov decision processes and global optimization problems. He has been investigating
new sampling and simulation-based techniques to overcome the computational difficulties
associated with traditional methods, where sampling and simulation techniques are
used not only to avoid enumerating the entire solution space but also to resolve the
issue of the unavailability of explicit mathematical models of the underlying systems.
For more information, see
Hu webpage.